Exploring The Evolving Role Of Data Engineers
Data Engineering Podcast
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Introduction
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Summary
Data Engineering is still a relatively new field that is going through a continued evolution as new technologies are introduced and new requirements are understood. In this episode Maxime Beauchemin returns to revisit what it means to be a data engineer and how the role has changed over the past 5 years.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Maxime Beauchemin about the impacts that the evolution of the modern data stack has had on the role and responsibilities of data engineers
Interview
- Introduction
- How did you get involved in the area of data management?
- What is your current working definition of a data engineer?
- How has that definition changed since your article on the "rise of the data engineer" and episode 3 of this show about "defining data engineering"?
- How has the growing availability of data infrastructure services shifted foundational skills and knowledge that are necessary to be effective?
- How should a new/aspiring data engineer focus their time and energy to become effective?
- One of the core themes in this current spate of technologies is "democratization of data". In your post on the downfall of the data engineer you called out the pressure on data engineers to maintain control with so many contributors with varying levels of skill and understanding. How well is the "modern data stack" balancing these concerns?
- An interesting impact of the growing usage of data is the constrained availability of data engineers. How do you see the effects of the job market on driving evolution of tooling and services?
- With the explosion of tools and services for working with data, a new problem has evolved of which ones to use for a given organization. What do you see as an effective and efficient process for enumerating and evaluating the available components for building a stack?
- There is also a lot of conversation around the "modern data stack", as well as the need for companies to build a "data platform". What (if any) difference do you see in the implications of those phrases and the skills required to compile a stack vs build a platform?
- How do you view the long term viability of templated SQL as a core workflow for transformations?
- What is the impact of more acessible and widespread machine learning/deep learning on data engineers/data infrastructure?
- How evenly distributed across industries and geographies are the advances in data infrastructure and engineering practices?
- What are some of the opportunities that are being missed or squandered during this dramatic shift in the data engineering landscape?
- What are the most interesting, innovative, or unexpected ways that you have seen the data ecosytem evolve?
- What are the most interesting, unexpected, or challenging lessons that you have learned while contributing to and participating in the data ecosystem?
- In episode 3 of this show (almost five years ago) we closed with some predictions for the following years of data engineering, many of which have been proven out. What is your retrospective on those claims, and what are your new predictions for the upcoming years?
Contact Info
- @mistercrunch on Twitter
- mistercrunch on GitHub
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- How the Modern Data Stack is Reshaping Data Engineering
- The Rise of the Data Engineer
- The Downfall of the Data Engineer
- Defining Data Engineering – Data Engineering Podcast
- Airflow
- Superset
- Preset
- Fivetran
- Meltano
- Airbyte
- Ralph Kimball
- Bill Inmon
- Feature Store
- Prophecy.io
- Ab Initio
- Dremio
- Data Mesh
- Firebolt
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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